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作 者:王拓[1] 戴连奎[1] 马万武[2] WANG Tuo;DAI Lian-Kui;MA Wan-Wu(State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China;Sinopec Qingdao Petrochemical Co.,Ltd,Qingdao 266043,China)
机构地区:[1]浙江大学工业控制技术国家重点实验室,杭州310027 [2]中国石化青岛石油化工有限责任公司,青岛266043
出 处:《分析化学》2018年第4期623-629,共7页Chinese Journal of Analytical Chemistry
摘 要:采用后向间隔偏最小二乘(Backward interval partial least squares,BiPLS)提取汽油拉曼光谱特征谱段,并用于研究法辛烷值(Research octane number,RON)的定量分析。实验中首先使用SPXY(Sample set partitioning based on joint x-y distances)方法划分训练集、交叉验证集和测试集,并采用稳健回归方法剔除异常的样本数据,再结合BiPLS方法筛选特征谱段,利用特征谱段建立偏最小二乘模型。与全谱段偏最小二乘模型的预测性能对比结果表明,后向间隔偏最小二乘方法可使输入模型的特征数据维数降低50.00%,交叉验证均方根误差(Root mean square error of cross validation,RMSECV)降低18.92%,预测均方根误差(Root mean square error of prediction,RMSEP)降低13.86%。后向间隔偏最小二乘方法可有效提取汽油拉曼光谱的特征谱段,降低模型复杂度,同时提高模型预测精度,在调和汽油研究法辛烷值定量分析方面有较好的应用前景。The feature of gasoline Raman spectra which were used to study the quantitative analysis of the research octane number(RON)were extracted for the first time using backward interval partial least squares(BiPLS).In the experiment,the sample set partitioning based on joint x-y distances(SPXY)method was used to divide the training set,the cross validation set and the test set.And the robust regression algorithm was used to remove the abnormal sample.The partial least squares model was established using feature selected by the BiPLS algorithm.Compared with the model without feature selection,it was shown that the backward interval partial least squares algorithm could reduce the input dimension by 50.00%,and the root mean square error of cross validation(RMSECV)by 18.92%and the root mean square error of prediction(RMSEP)by 13.86%.The backward interval partial least squares algorithm can effectively extract the feature from gasoline Raman spectrum,reduce the model complexity,and improve the prediction accuracy of the model,and has great application prospect in the quantitative analysis of research octane number.
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